Multiple realization history matching has revolutionized the approach to reservoir simulation studies in the field of hydrocarbon fuel production. In general, multiple realization history matching attempts to approximate an idealized problem that can be represented by Bayes' Theorem. Examples of conventional multiple realization history matching methods and systems include those described, for example, in U.S. Pat. No. 7,532,984 issued to Syngaevshy, U.S. Pat. No. 7,448,262 issued to Sheng et al., and U.S. Patent Application Publication No. 2008/0077371 by Yeten et al.
A problem remains, however, that due to the relatively large number of input parameters involved in many multiple realization history matching problems, including reservoir studies, even with huge advances in computing power, the existing approaches (which rely on sampling ever more realizations) typically cannot fully populate the probability distributions that they claim to be using in their solution. Conventional approaches for dealing with undesirably few available samples include (1) taking the samples and tweaking them to match the production history (e.g. commercial software for this includes MEPO by Scandpower Petroleum Technology, and SimOpt from Schlumberger Information Solutions (SIS)), (2) interpolating a function through the available samples and then sampling that function instead of running a simulator (e.g. available commercial software includes COUGAR from Innovation Energie Environnement), and (3) ranking the available realizations using, for example, a root-mean-square fit to the data (e.g. using a variety of suitable applications, including Petrel-RE available from SIS). Although desirable results are being achieved using such conventional systems and methods, there is room for improvement.